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HAM: a deep collaborative ranking method incorporating textual information Research Articles

Cheng-wei Wang, Teng-fei Zhou, Chen Chen, Tian-lei Hu, Gang Chen,rr@zju.edu.cn,zhoutengfei@zju.edu.cn,cc33@zju.edu.cn,htl@zju.edu.cn,cg@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8,   Pages 1119-1266 doi: 10.1631/FITEE.1900382

Abstract: The recommendation task with a textual corpus aims to model customer preferences from both user feedback and item textual descriptions. It is highly desirable to explore a very deep neural network to capture the complicated nonlinear preferences. However, training a deeper recommender is not as effortless as simply adding layers. A deeper recommender suffers from the gradient vanishing/exploding issue and cannot be easily trained by gradient-based methods. Moreover, textual descriptions probably contain noisy word sequences. Directly extracting feature vectors from them can harm the recommender’s performance. To overcome these difficulties, we propose a new recommendation method named the HighwAy recoMmender (HAM). HAM explores a highway mechanism to make gradient-based training methods stable. A multi-head attention mechanism is devised to automatically denoise textual information. Moreover, a method is devised to train a deep neural recommender. Empirical studies show that the proposed method outperforms state-of-the-art methods significantly in terms of accuracy.

Keywords： 深度学习；推荐系统；高速公路网络；块坐标梯度下降    

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Rational understanding about the way to revitalize China’s road and bridge construction

Feng Maorun,Zhao Zhengsong

Strategic Study of CAE 2013, Volume 15, Issue 11,   Pages 36-43

Abstract:

At the turn of this century,China began to undertake the largest scale road and bridge construction in the world,which has virtuously interacted with the social and economic development and has brought the construction technology up to the world advanced level. Looking back to the world transportation development and comparing the road and bridge construction in Europe,USA,Japan and China,the common successful experiences can be obtained as follows: it has become the value orientation for transportation engineering to give the priority to the transportation for economic development;it has become the national strategy and social action to build up great transportation artery;demand and innovation are the driving force to upgrade road and bridge construction technology;management innovation and quality control have upgraded the project construction quality. While rationally recognizing the achievements in the transportation construction of our country,we should review the deep rooted problems with China’s characteristics that have affected construction quality and efficiency;reveal any construction behavior that violates scientific law and extensive management in road and bridge construction and maintenance;probe into the new challenges to strengthen social management and create healthy soft environment.

Keywords： road network     expressway     bridge project     technological innovation     construction management     social management    

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The Necessity of ITS Adopted in Expressway

Song Ke,Shao Peiji

Strategic Study of CAE 2002, Volume 4, Issue 12,   Pages 77-79

Abstract:

With the improvement of China's expressway, how to use expressway economically and safely has became an important problem. Setting up more roads simply, and extending the road net scale can not meet the increasing transportation need. Application of Hi-tech, e.g. modern information and communication technique, to reform the current road system and its management system, can significantly increase the traffic capacity and the service quality of road net. Intelligence transportation system (ITS) is a direction to transport development, and also an important measure to solve the transportation conveyance bottleneck. This thesis, based on the detailed data, analyzed present developing condition of China’s expressway first, then analyzed the overseas expressway development experience, and finally expatiated the necessity of ITS used in China´s expressway by taking the Sichuan Province ITS project as an example.

Keywords： expressway     ITS     necessity    

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Research on the evaluation and mechanism of socio economic benefit of highway projects

Wang Yuning,Yun Yingxia,Fan Zhiqing

Strategic Study of CAE 2012, Volume 14, Issue 10,   Pages 97-102

Abstract:

Based on the theory of system dynamics, the paper analyzes the mechanism of socio economic benefits of highway projects and establishes the system dynamic model of regional economic－public road transportation. Then taking Jinji highway project of Tianjin as an example, the error of system simulation tested and the system dynamic model built are verified to be quite stable, which have a high performance. Through the comparison of simulation results of Jinji highway being built or not, the paper simulates and predicts the socio economic benefit of each year from 2003 to 2013. Thus the quantification evaluation of socio economic benefit of highway project is realized and it will provide the theory instructions for similar projects in the future.

Keywords： highway project     socio economic benefit     evaluation     system dynamics    

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Highway Planning and Design in the Qinghai–Tibet Plateau of China: A Cost-Safety Balance Perspective Article

Chengqian Li, Lieyun Ding, Botao Zhong

Engineering 2019, Volume 5, Issue 2,   Pages 337-349 doi: 10.1016/j.eng.2018.12.008

Abstract:

Engineering designs for mountainous highways emphasize compliance checking to ensure safety. However, relying
solely on compliance checking may lead designers to minimize costs at the expense of high risk indicators, since the overall risk level of the highway design is unknown to the designers. This paper describes a method for the simultaneous consideration of traffic safety risks and the associated cost burden related to the appropriate planning and design of a mountainous highway. The method can be carried out in four steps: First, the highway design is represented by a new parametric framework to extract the key design variables that affect not only the life-cycle cost but also the operational safety. Second, the relationship between the life-cycle cost and the operational safety risk factors is established in the cost-estimation functions. Third, a fault tree analysis (FTA) is introduced to identify the traffic risk factors from the design variables. The safety performance of the design solutions is also assessed by the generalized linear-regression model. Fourth, a theory of acceptable risk analysis is introduced to the traffic safety assessment, and a computing algorithm is proposed to solve for a cost-efficient optimal solution within the range of acceptable risk, in order to help decision-makers. This approach was applied and examined in the Sichuan-Tibet Highway engineering project, which is located in a complex area with a large elevation gradient and a wide range of mountains. The experimental results show that the proposed approach significantly improved both the safety and cost performance of the project in the study area.

Keywords： Highway planning and design     Cost-safety optimization     Acceptable risk assessment     Sichuan-Tibet Highway    

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Expressway extension project construction management based on the theory of sustainable development

Dong Jianjun,Cheng Hu

Strategic Study of CAE 2011, Volume 13, Issue 3,   Pages 97-103

Abstract:

The paper expounded the extension and connotation of sustainable development conception of expressway extension project. In allusion to the stratagem objective of sustainable development, the objective system of sustainable development was constructed and expressway extension project was studied as programme system. The paper constructed the life-cycle integrated management system of expressway extension project. Around the three-dimensional coordinate system of integrated management the meaning of expressway extension project life-cycle was explained. The programme characteristics were analyzed and an incorporate organizational structure of "extension—operation—traffic management"  was built. The paper built information structure matrix and information management system of expressway extension project life-cycle and used weatherglass to build an evaluation model of sustainable development. At last, the inspirer was summarized which the research on expressway extension project construction management based on the theory of sustainable development can offer to the construction of engineering after and the development of industry.

Keywords： sustainable development     expressway extension project     life-cycle     integrated management    

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: The collaborative filtering technology used in traditional systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional algorithms, thus leading to the emergence of systems based on . At present, s mostly use deep s to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the . Aimed at this problem, in this paper we propose a feedforward deep method, called the deep association (DAN), which is based on the joint action of multiple categories of information, for implicit feedback . Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint s can provide better performance.

Keywords： Neural network     Deep learning     Deep association neural network (DAN)     Recommendation    

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Corrugated steel pipe culvert applying in highway and strain measuring with fiber grating test technology

Cheng Gang,Fang Ping

Strategic Study of CAE 2013, Volume 15, Issue 8,   Pages 108-112

Abstract:

Two corrugated steel pipe culverts were chosen in north connection highway project of the Fourth Nanjing Yangtze River Bridge. From testing the strain conditions of corrugated steel pipe culvert under different soil fill heights, the fiber Bragg grating sensors were set in the inner or outer of corrugated steel pipe culverts. Corrugated steel pipe culvert has the capacity of larger formation. So in the soft soil area, using its good ductility can solve the problem of uneven settlement.

Keywords： corrugated steel pipe culvert     highway     soft foundation     field test     fiber test    

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Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting Artical

Longbing Cao

Engineering 2016, Volume 2, Issue 2,   Pages 212-224 doi: 10.1016/J.ENG.2016.02.013

Abstract:

While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.

Keywords： Independent and identically distributed (IID)     Non-IID     Heterogeneity     Coupling relationship     Coupling learning     Relational learning     IIDness learning     Non-IIDness learning     Recommender system     Recommendation     Non-IID recommendation    

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Block coordinate descent with time perturbation for nonconvex nonsmooth problems in real-world studies Research Article

Rui Liu, Wei-chu Sun, Tao Hou, Chun-hong Hu, Lin-bo Qiao,liuruirui@csu.edu.cn,smsysun@foxmail.com,houtao@csu.edu.cn,huchunhong@csu.edu.cn,qiao.linbo@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 10,   Pages 1390-1403 doi: 10.1631/FITEE.1900341

Abstract: The era of big data in healthcare is here, and this era will significantly improve medicine and especially oncology. However, traditional machine learning algorithms need to be promoted to solve such large-scale real-world problems due to a large amount of data that needs to be analyzed and the difficulty in solving problems with nonconvex nonlinear settings. We aim to minimize the composite of a smooth nonlinear function and a block-separable nonconvex function on a large number of block variables with inequality constraints. We propose a novel parallel first-order optimization method, called asynchronous block coordinate descent with (ATP), which adopts a technique that escapes from saddle points and sub-optimal local points. The details of the proposed method are presented with analyses of convergence and iteration complexity properties. Experiments conducted on real-world machine learning problems validate the efficacy of our proposed method. The experimental results demonstrate that enables ATP to escape from saddle points and sub-optimal points, providing a promising way to handle nonconvex optimization problems with inequality constraints employing asynchronous block coordinate descent. The asynchronous parallel implementation on shared memory multi-core platforms indicates that the proposed algorithm, ATP, has strong scalability.

Keywords： 收敛分析；异步块坐标下降法；时间扰动；非凸非平滑优化；真实世界研究    

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Diffractive Deep Neural Networks at Visible Wavelengths Article

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Engineering 2021, Volume 7, Issue 10,   Pages 1485-1493 doi: 10.1016/j.eng.2020.07.032

Abstract:

Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing, computational speed, and power efficiency. One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing technology operated in the terahertz spectral range. Since the terahertz bandwidth involves limited interparticle coupling and material losses, this paper
extends D2NN to visible wavelengths. A general theory including a revised formula is proposed to solve any contradictions between wavelength, neuron size, and fabrication limitations. A novel visible light D2NN classifier is used to recognize unchanged targets (handwritten digits ranging from 0 to 9) and targets that have been changed (i.e., targets that have been covered or altered) at a visible wavelength of 632.8 nm. The obtained experimental classification accuracy (84%) and numerical classification accuracy (91.57%) quantify the match between the theoretical design and fabricated system performance. The presented framework can be used to apply a D2NN to various practical applications and design other new applications.

Keywords： Optical computation     Optical neural networks     Deep learning     Optical machine learning     Diffractive deep neural networks    

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Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water Systems in Smart Grids Article

Junjie Hu, Huayanran Zhou, Yihong Zhou, Haijing Zhang, Lars Nordströmd, Guangya Yang

Engineering 2021, Volume 7, Issue 8,   Pages 1101-1114 doi: 10.1016/j.eng.2021.06.008

Abstract:

With the growth of intermittent renewable energy generation in power grids, there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability. The flexibility of demand response (DR) resources has become a valuable solution to this problem. However, existing research indicates that problems on flexibility prediction of DR resources have not been investigated. This study applied the temporal convolution network (TCN)-combined transformer, a deep learning technique to predict the aggregated flexibility of two types of DR resources, that is, electric vehicles (EVs) and domestic hot water system (DHWS). The prediction uses historical power consumption data of these DR resources and DR signals (DS) to facilitate prediction. The prediction can generate the size and maintenance time of the aggregated flexibility. The accuracy of the flexibility prediction results was verified through simulations of case studies. The simulation results show that under different maintenance times, the size of the flexibility changed. The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

Keywords： Load flexibility     Electric vehicles     Domestic hot water system     Temporal convolution network-combined transformer     Deep learning    

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Adversarial Attacks and Defenses in Deep Learning Feature Article

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Engineering 2020, Volume 6, Issue 3,   Pages 346-360 doi: 10.1016/j.eng.2019.12.012

Abstract:

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical
to ensure the security and robustness of the deployed algorithms. Recently, the security vulnerability of
DL algorithms to adversarial samples has been widely recognized. The fabricated samples can lead to various
misbehaviors of the DL models while being perceived as benign by humans. Successful implementations
of adversarial attacks in real physical-world scenarios further demonstrate their practicality.
Hence, adversarial attack and defense techniques have attracted increasing attention from both machine
learning and security communities and have become a hot research topic in recent years. In this paper,
we first introduce the theoretical foundations, algorithms, and applications of adversarial attack techniques.
We then describe a few research efforts on the defense techniques, which cover the broad frontier
in the field. Several open problems and challenges are subsequently discussed, which we hope will provoke
further research efforts in this critical area.

Keywords： Machine learning     Deep neural network Adversarial example     Adversarial attack     Adversarial defense    

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A descent method for the Dubins traveling salesman problem with neighborhoods Research Articles

Zheng Chen, Chen-hao Sun, Xue-ming Shao, Wen-jie Zhao,z_chen@zju.edu.cn,mecsxm@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000041

Abstract: In this study, we focus mainly on the problem of finding the minimum-length path through a set of circular regions by a fixed-wing unmanned aerial vehicle. Such a problem is referred to as the with neighborhoods (DTSPN). Algorithms developed in the literature for solving DTSPN either are computationally demanding or generate low-quality solutions. To achieve a better trade-off between solution quality and computational cost, an efficient gradient-free is designed. The core idea of the is to decompose DTSPN into a series of subproblems, each of which consists of finding the minimum-length path of a from a configuration to another configuration via an intermediate circular region. By analyzing the geometric properties of the subproblems, we use a bisection method to solve the subproblems. As a result, the can efficiently address DTSPN by successively solving a series of subproblems. Finally, several numerical experiments are carried out to demonstrate the in comparison with several existing algorithms.

Keywords： Dubins飞行器；坐标下降法；Dubins旅行商问题    

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Deep 3D reconstruction: methods, data, and challenges Review Article

Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000068

Abstract: Three-dimensional (3D) reconstruction of shapes is an important research topic in the fields of computer vision, computer graphics, pattern recognition, and virtual reality. Existing 3D reconstruction methods usually suffer from two bottlenecks: (1) they involve multiple manually designed states which can lead to cumulative errors, but can hardly learn semantic features of 3D shapes automatically; (2) they depend heavily on the content and quality of images, as well as precisely calibrated cameras. As a result, it is difficult to improve the reconstruction accuracy of those methods. 3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep networks. However, while these methods have various architectures, in-depth analysis and comparisons of them are unavailable so far. We present a comprehensive survey of 3D reconstruction methods based on deep learning. First, based on different deep learning model architectures, we divide 3D reconstruction methods based on deep learning into four types, , , , and based methods, and analyze the corresponding methodologies carefully. Second, we investigate four representative databases that are commonly used by the above methods in detail. Third, we give a comprehensive comparison of 3D reconstruction methods based on deep learning, which consists of the results of different methods with respect to the same database, the results of each method with respect to different databases, and the robustness of each method with respect to the number of views. Finally, we discuss future development of 3D reconstruction methods based on deep learning.

Keywords： 深度学习模型；三维重建；循环神经网络；深度自编码器；生成对抗网络；卷积神经网络    

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Title Author Date Type Operation

HAM: a deep collaborative ranking method incorporating textual information

Cheng-wei Wang, Teng-fei Zhou, Chen Chen, Tian-lei Hu, Gang Chen,rr@zju.edu.cn,zhoutengfei@zju.edu.cn,cc33@zju.edu.cn,htl@zju.edu.cn,cg@zju.edu.cn

Journal Article

Rational understanding about the way to revitalize China’s road and bridge construction

Feng Maorun,Zhao Zhengsong

Journal Article

The Necessity of ITS Adopted in Expressway

Song Ke,Shao Peiji

Journal Article

Research on the evaluation and mechanism of socio economic benefit of highway projects

Wang Yuning,Yun Yingxia,Fan Zhiqing

Journal Article

Highway Planning and Design in the Qinghai–Tibet Plateau of China: A Cost-Safety Balance Perspective

Chengqian Li, Lieyun Ding, Botao Zhong

Journal Article

Expressway extension project construction management based on the theory of sustainable development

Dong Jianjun,Cheng Hu

Journal Article

DAN: a deep association neural network approach for personalization recommendation

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Journal Article

Corrugated steel pipe culvert applying in highway and strain measuring with fiber grating test technology

Cheng Gang,Fang Ping

Journal Article

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Longbing Cao

Journal Article

Block coordinate descent with time perturbation for nonconvex nonsmooth problems in real-world studies

Rui Liu, Wei-chu Sun, Tao Hou, Chun-hong Hu, Lin-bo Qiao,liuruirui@csu.edu.cn,smsysun@foxmail.com,houtao@csu.edu.cn,huchunhong@csu.edu.cn,qiao.linbo@nudt.edu.cn

Journal Article

Diffractive Deep Neural Networks at Visible Wavelengths

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Journal Article

Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water Systems in Smart Grids

Junjie Hu, Huayanran Zhou, Yihong Zhou, Haijing Zhang, Lars Nordströmd, Guangya Yang

Journal Article

Adversarial Attacks and Defenses in Deep Learning

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Journal Article

A descent method for the Dubins traveling salesman problem with neighborhoods

Zheng Chen, Chen-hao Sun, Xue-ming Shao, Wen-jie Zhao,z_chen@zju.edu.cn,mecsxm@zju.edu.cn

Journal Article

Deep 3D reconstruction: methods, data, and challenges

Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn

Journal Article